class TestHBOS(unittest.TestCase):
def setUp(self):
self.n_train = 200
self.n_test = 100
self.contamination = 0.1
self.roc_floor = 0.8
self.X_train, self.y_train, self.X_test, self.y_test = generate_data(
n_train=self.n_train,
n_test=self.n_test,
contamination=self.contamination,
random_state=42)
self.clf = HBOS(contamination=self.contamination)
self.clf.fit(self.X_train)
def test_parameters(self):
assert (hasattr(self.clf, 'decision_scores_')
and self.clf.decision_scores_ is not None)
assert (hasattr(self.clf, 'labels_') and self.clf.labels_ is not None)
assert (hasattr(self.clf, 'threshold_')
and self.clf.threshold_ is not None)
assert (hasattr(self.clf, '_mu') and self.clf._mu is not None)
assert (hasattr(self.clf, '_sigma') and self.clf._sigma is not None)
assert (hasattr(self.clf, 'hist_') and self.clf.hist_ is not None)
assert (hasattr(self.clf, 'bin_edges_')
and self.clf.bin_edges_ is not None)
def test_train_scores(self):
assert_equal(len(self.clf.decision_scores_), self.X_train.shape[0])
def test_prediction_scores(self):
pred_scores = self.clf.decision_function(self.X_test)
# check score shapes
assert_equal(pred_scores.shape[0], self.X_test.shape[0])
# check performance
assert (roc_auc_score(self.y_test, pred_scores) >= self.roc_floor)
def test_prediction_labels(self):
pred_labels = self.clf.predict(self.X_test)
assert_equal(pred_labels.shape, self.y_test.shape)
def test_prediction_proba(self):
pred_proba = self.clf.predict_proba(self.X_test)
assert (pred_proba.min() >= 0)
assert (pred_proba.max() <= 1)
def test_prediction_proba_linear(self):
pred_proba = self.clf.predict_proba(self.X_test, method='linear')
assert (pred_proba.min() >= 0)
assert (pred_proba.max() <= 1)
def test_prediction_proba_unify(self):
pred_proba = self.clf.predict_proba(self.X_test, method='unify')
assert (pred_proba.min() >= 0)
assert (pred_proba.max() <= 1)
def test_prediction_proba_parameter(self):
with assert_raises(ValueError):
self.clf.predict_proba(self.X_test, method='something')
def test_fit_predict(self):
pred_labels = self.clf.fit_predict(self.X_train)
assert_equal(pred_labels.shape, self.y_train.shape)
def test_fit_predict_score(self):
self.clf.fit_predict_score(self.X_test, self.y_test)
self.clf.fit_predict_score(self.X_test,
self.y_test,
scoring='roc_auc_score')
self.clf.fit_predict_score(self.X_test,
self.y_test,
scoring='prc_n_score')
with assert_raises(NotImplementedError):
self.clf.fit_predict_score(self.X_test,
self.y_test,
scoring='something')
# def test_score(self):
# self.clf.score(self.X_test, self.y_test)
# self.clf.score(self.X_test, self.y_test, scoring='roc_auc_score')
# self.clf.score(self.X_test, self.y_test, scoring='prc_n_score')
# with assert_raises(NotImplementedError):
# self.clf.score(self.X_test, self.y_test, scoring='something')
def test_predict_rank(self):
pred_socres = self.clf.decision_function(self.X_test)
pred_ranks = self.clf._predict_rank(self.X_test)
# assert the order is reserved
assert_allclose(rankdata(pred_ranks), rankdata(pred_socres), atol=2)
assert_array_less(pred_ranks, self.X_train.shape[0] + 1)
assert_array_less(-0.1, pred_ranks)
def test_predict_rank_normalized(self):
pred_socres = self.clf.decision_function(self.X_test)
pred_ranks = self.clf._predict_rank(self.X_test, normalized=True)
# assert the order is reserved
assert_allclose(rankdata(pred_ranks), rankdata(pred_socres), atol=2)
assert_array_less(pred_ranks, 1.01)
assert_array_less(-0.1, pred_ranks)
def test_model_clone(self):
clone_clf = clone(self.clf)
def tearDown(self):
pass
class TestHBOS(unittest.TestCase):
def setUp(self):
self.n_train = 100
self.n_test = 50
self.contamination = 0.1
self.roc_floor = 0.6
self.X_train, self.y_train, self.X_test, self.y_test = generate_data(
n_train=self.n_train,
n_test=self.n_test,
contamination=self.contamination)
self.clf = HBOS(contamination=self.contamination)
self.clf.fit(self.X_train)
def test_sklearn_estimator(self):
check_estimator(self.clf)
def test_parameters(self):
if not hasattr(
self.clf,
'decision_scores_') or self.clf.decision_scores_ is None:
self.assertRaises(AttributeError, 'decision_scores_ is not set')
if not hasattr(self.clf, 'labels_') or self.clf.labels_ is None:
self.assertRaises(AttributeError, 'labels_ is not set')
if not hasattr(self.clf, 'threshold_') or self.clf.threshold_ is None:
self.assertRaises(AttributeError, 'threshold_ is not set')
if not hasattr(self.clf, '_mu') or self.clf._mu is None:
self.assertRaises(AttributeError, '_mu is not set')
if not hasattr(self.clf, '_sigma') or self.clf._sigma is None:
self.assertRaises(AttributeError, '_sigma is not set')
if not hasattr(self.clf, 'hist_') or self.clf.hist_ is None:
self.assertRaises(AttributeError, 'hist_ is not set')
if not hasattr(self.clf, 'bin_edges_') or self.clf.bin_edges_ is None:
self.assertRaises(AttributeError, 'bin_edges_ is not set')
def test_train_scores(self):
assert_equal(len(self.clf.decision_scores_), self.X_train.shape[0])
def test_prediction_scores(self):
pred_scores = self.clf.decision_function(self.X_test)
# check score shapes
assert_equal(pred_scores.shape[0], self.X_test.shape[0])
# check performance
assert_greater(roc_auc_score(self.y_test, pred_scores), self.roc_floor)
def test_prediction_labels(self):
pred_labels = self.clf.predict(self.X_test)
assert_equal(pred_labels.shape, self.y_test.shape)
def test_prediction_proba(self):
pred_proba = self.clf.predict_proba(self.X_test)
assert_greater_equal(pred_proba.min(), 0)
assert_less_equal(pred_proba.max(), 1)
def test_prediction_proba_linear(self):
pred_proba = self.clf.predict_proba(self.X_test, method='linear')
assert_greater_equal(pred_proba.min(), 0)
assert_less_equal(pred_proba.max(), 1)
def test_prediction_proba_unify(self):
pred_proba = self.clf.predict_proba(self.X_test, method='unify')
assert_greater_equal(pred_proba.min(), 0)
assert_less_equal(pred_proba.max(), 1)
def test_prediction_proba_parameter(self):
with assert_raises(ValueError):
self.clf.predict_proba(self.X_test, method='something')
def test_fit_predict(self):
pred_labels = self.clf.fit_predict(self.X_train)
assert_equal(pred_labels.shape, self.y_train.shape)
def test_evaluate(self):
self.clf.fit_predict_evaluate(self.X_test, self.y_test)
def tearDown(self):
pass
class TestHBOS(unittest.TestCase):
def setUp(self):
self.n_train = 100
self.n_test = 50
self.contamination = 0.1
self.roc_floor = 0.6
self.X_train, self.y_train, self.X_test, self.y_test = generate_data(
n_train=self.n_train, n_test=self.n_test,
contamination=self.contamination, random_state=42)
self.clf = HBOS(contamination=self.contamination)
self.clf.fit(self.X_train)
def test_sklearn_estimator(self):
check_estimator(self.clf)
def test_parameters(self):
assert_true(hasattr(self.clf, 'decision_scores_') and
self.clf.decision_scores_ is not None)
assert_true(hasattr(self.clf, 'labels_') and
self.clf.labels_ is not None)
assert_true(hasattr(self.clf, 'threshold_') and
self.clf.threshold_ is not None)
assert_true(hasattr(self.clf, '_mu') and
self.clf._mu is not None)
assert_true(hasattr(self.clf, '_sigma') and
self.clf._sigma is not None)
assert_true(hasattr(self.clf, 'hist_') and
self.clf.hist_ is not None)
assert_true(hasattr(self.clf, 'bin_edges_') and
self.clf.bin_edges_ is not None)
def test_train_scores(self):
assert_equal(len(self.clf.decision_scores_), self.X_train.shape[0])
def test_prediction_scores(self):
pred_scores = self.clf.decision_function(self.X_test)
# check score shapes
assert_equal(pred_scores.shape[0], self.X_test.shape[0])
# check performance
assert_greater(roc_auc_score(self.y_test, pred_scores), self.roc_floor)
def test_prediction_labels(self):
pred_labels = self.clf.predict(self.X_test)
assert_equal(pred_labels.shape, self.y_test.shape)
def test_prediction_proba(self):
pred_proba = self.clf.predict_proba(self.X_test)
assert_greater_equal(pred_proba.min(), 0)
assert_less_equal(pred_proba.max(), 1)
def test_prediction_proba_linear(self):
pred_proba = self.clf.predict_proba(self.X_test, method='linear')
assert_greater_equal(pred_proba.min(), 0)
assert_less_equal(pred_proba.max(), 1)
def test_prediction_proba_unify(self):
pred_proba = self.clf.predict_proba(self.X_test, method='unify')
assert_greater_equal(pred_proba.min(), 0)
assert_less_equal(pred_proba.max(), 1)
def test_prediction_proba_parameter(self):
with assert_raises(ValueError):
self.clf.predict_proba(self.X_test, method='something')
def test_fit_predict(self):
pred_labels = self.clf.fit_predict(self.X_train)
assert_equal(pred_labels.shape, self.y_train.shape)
def test_fit_predict_score(self):
self.clf.fit_predict_score(self.X_test, self.y_test)
self.clf.fit_predict_score(self.X_test, self.y_test,
scoring='roc_auc_score')
self.clf.fit_predict_score(self.X_test, self.y_test,
scoring='prc_n_score')
with assert_raises(NotImplementedError):
self.clf.fit_predict_score(self.X_test, self.y_test,
scoring='something')
# def test_score(self):
# self.clf.score(self.X_test, self.y_test)
# self.clf.score(self.X_test, self.y_test, scoring='roc_auc_score')
# self.clf.score(self.X_test, self.y_test, scoring='prc_n_score')
# with assert_raises(NotImplementedError):
# self.clf.score(self.X_test, self.y_test, scoring='something')
def test_predict_rank(self):
pred_socres = self.clf.decision_function(self.X_test)
pred_ranks = self.clf._predict_rank(self.X_test)
# assert the order is reserved
assert_allclose(rankdata(pred_ranks), rankdata(pred_socres), atol=2)
assert_array_less(pred_ranks, self.X_train.shape[0] + 1)
assert_array_less(-0.1, pred_ranks)
def test_predict_rank_normalized(self):
pred_socres = self.clf.decision_function(self.X_test)
pred_ranks = self.clf._predict_rank(self.X_test, normalized=True)
# assert the order is reserved
assert_allclose(rankdata(pred_ranks), rankdata(pred_socres), atol=2)
assert_array_less(pred_ranks, 1.01)
assert_array_less(-0.1, pred_ranks)
def tearDown(self):
pass
def do_pyod(model, colnames, arr_baseline, arr_highlight):
# init some counters
n_charts, n_dims, n_bad_data, fit_success, fit_default, fit_fail = init_counters(
colnames)
# dict to collect results into
results = {}
n_lags = model.get('n_lags', 0)
model_level = model.get('model_level', 'dim')
model = model.get('type', 'hbos')
# model init
clf = pyod_init(model)
# get map of cols to loop over
col_map = get_col_map(colnames, model_level)
# build each model
for colname in col_map:
chart = colname.split('|')[0]
dimension = colname.split('|')[1] if '|' in colname else '*'
arr_baseline_dim = arr_baseline[:, col_map[colname]]
arr_highlight_dim = arr_highlight[:, col_map[colname]]
# check for bad data
bad_data = False
# skip if bad data
if bad_data:
n_bad_data += 1
log.info(f'... skipping {colname} due to bad data')
else:
if n_lags > 0:
arr_baseline_dim = add_lags(arr_baseline_dim, n_lags=n_lags)
arr_highlight_dim = add_lags(arr_highlight_dim, n_lags=n_lags)
# remove any nan rows
arr_baseline_dim = arr_baseline_dim[~np.isnan(arr_baseline_dim).
any(axis=1)]
arr_highlight_dim = arr_highlight_dim[~np.isnan(arr_highlight_dim).
any(axis=1)]
log.debug(f'... chart = {chart}')
log.debug(f'... dimension = {dimension}')
log.debug(f'... arr_baseline_dim.shape = {arr_baseline_dim.shape}')
log.debug(
f'... arr_highlight_dim.shape = {arr_highlight_dim.shape}')
log.debug(f'... arr_baseline_dim = {arr_baseline_dim}')
log.debug(f'... arr_highlight_dim = {arr_highlight_dim}')
if model == ['auto_encoder']:
clf = pyod_init(model, n_features=arr_baseline_dim.shape[1])
clf, result = try_fit(clf, colname, arr_baseline_dim,
PyODDefaultModel)
fit_success += 1 if result == 'success' else 0
fit_default += 1 if result == 'default' else 0
# try predictions and if they fail use default model
try:
preds = clf.predict(arr_highlight_dim)
probs = clf.predict_proba(arr_highlight_dim)[:, 1]
except:
fit_success -= 1
fit_default += 1
clf = PyODDefaultModel()
clf.fit(arr_baseline_dim)
preds = clf.predict(arr_highlight_dim)
probs = clf.predict_proba(arr_highlight_dim)[:, 1]
log.debug(f'... preds.shape = {preds.shape}')
log.debug(f'... preds = {preds}')
log.debug(f'... probs.shape = {probs.shape}')
log.debug(f'... probs = {probs}')
# save results
score = (np.mean(probs) + np.mean(preds)) / 2
if chart in results:
results[chart].append({dimension: {'score': score}})
else:
results[chart] = [{dimension: {'score': score}}]
# log some summary stats
log.info(
summary_info(n_charts, n_dims, n_bad_data, fit_success, fit_fail,
fit_default, model_level))
return results